CN111046146B - Method and device for generating information - Google Patents
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- CN111046146B CN111046146B CN201911374338.2A CN201911374338A CN111046146B CN 111046146 B CN111046146 B CN 111046146B CN 201911374338 A CN201911374338 A CN 201911374338A CN 111046146 B CN111046146 B CN 111046146B
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Abstract
The embodiment of the disclosure discloses a method and a device for generating information. One embodiment of the method comprises the following steps: receiving dialogue content input by a user; determining a domain to which the dialogue content belongs based on the dialogue content; determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs, wherein the semantic analysis service is used for carrying out semantic analysis on the content in the specific domain; and carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result. The implementation method avoids resource waste caused by calling all semantic analysis services.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for generating information.
Background
Commercial human-machine dialog systems often have complex and versatile functions. For example, a certain map intelligent voice assistant not only has the function of navigation, but also has the functions of weather searching, laughing and the like. In practical applications, for each individual functional domain, it is generally necessary to use a corresponding semantic parsing service for semantic parsing. In this way, by using a plurality of semantic analysis services in different fields and a dialogue central control mode, the input request of the user is forwarded to all the semantic analysis services, and the analysis result is processed by the dialogue central control, so that the multi-field semantic analysis capability of the man-machine dialogue system is realized. However, as the man-machine interactive system becomes more complex and the number of fields increases, the man-machine interactive system can be directly provided with multi-field parsing capability by a simple manner of calling all semantic parsing services. In this manner, however, each user request requires invocation of all semantic parsing services. In fact, only one or two corresponding semantic parsing services can return results each time a user requests, and other semantic parsing services do not need to be called. Therefore, in a complex man-machine conversation system, the consumed resources in the mode are proportional to the number of fields, and great resource waste is caused.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for generating information.
In a first aspect, embodiments of the present disclosure provide a method for generating information, the method comprising: receiving dialogue content input by a user; determining a domain to which the dialogue content belongs based on the dialogue content; determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs, wherein the semantic analysis service is used for carrying out semantic analysis on the content in the specific domain; and carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result.
In some embodiments, the determining, based on the dialog content, a domain to which the dialog content belongs includes: inputting the dialogue content into a pre-established domain classification model to obtain a multidimensional vector, wherein a plurality of dimensions in the multidimensional vector are in one-to-one correspondence with a plurality of preset domains, and the values of elements of the multidimensional vector are probability values of the corresponding domains of the dialogue content; matching the dialogue content with semantic templates in a semantic template set preset for the field, and determining a field identification result for the dialogue content based on the matching result; and determining the domain to which the conversation content belongs based on the multidimensional vector and the domain identification result.
In some embodiments, determining the domain identification result for the dialog content based on the matching result includes: in response to determining that the dialog content matches a semantic template in the set of semantic templates, calculating a weight of text in the dialog content that matches the semantic template to text corresponding to the dialog content; in response to determining that the specific gravity exceeds a preset specific gravity threshold, the domain corresponding to the matched semantic template is used as a domain identification result.
In some embodiments, the determining the domain to which the dialog content belongs based on the multidimensional vector and the domain identification result includes: setting a probability of a domain included in the domain identification result to a fixed probability value in response to determining that at least one domain is included in the domain identification result; determining final probability values of a plurality of fields according to the probability values of the fields represented by the elements of the multidimensional vector and the fixed probability values of the fields included in the field identification result; and determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
In some embodiments, the domain classification model is trained by: obtaining a sample set, wherein the sample comprises a sample multidimensional vector and a sample sentence; and taking the sample sentences in the sample set as input, taking the sample multidimensional vectors corresponding to the input sample sentences as expected output, and training to obtain the domain classification model.
In some embodiments, the samples in the sample set are obtained by: using the at least one semantic analysis service to perform semantic analysis on the sample sentence; and generating a sample multidimensional vector for the sample sentence according to the analysis result of the semantic analysis service in the at least one semantic analysis service.
In a second aspect, an embodiment of the present disclosure provides an apparatus for generating information, the apparatus including: a receiving unit configured to receive dialogue content input by a user; a first determination unit configured to determine, based on the dialogue content, a domain to which the dialogue content belongs; a second determining unit configured to determine, from at least one semantic analysis service, a semantic analysis service as a target semantic analysis service according to a domain to which the dialog content belongs, where the semantic analysis service is used for performing semantic analysis on the content in a specific domain; and the generation unit is configured to perform semantic analysis on the dialogue content by using the target semantic analysis service and generate a semantic analysis result.
In some embodiments, the first determining unit includes: the input unit is configured to input the dialogue content into a pre-established domain classification model to obtain a multi-dimensional vector, wherein a plurality of dimensions in the multi-dimensional vector are in one-to-one correspondence with a plurality of preset domains, and values of elements of the multi-dimensional vector are probability values of the corresponding domains to which the dialogue content belongs; a matching unit configured to match the dialog content with a semantic template in a semantic template set preset for a domain, and determine a domain identification result for the dialog content based on the matching result; and a determination subunit configured to determine, based on the multidimensional vector and the domain identification result, a domain to which the dialogue content belongs.
In some embodiments, the matching unit is further configured to: in response to determining that the dialog content matches a semantic template in the set of semantic templates, calculating a weight of text in the dialog content that matches the semantic template to text corresponding to the dialog content; in response to determining that the specific gravity exceeds a preset specific gravity threshold, the domain corresponding to the matched semantic template is used as a domain identification result.
In some embodiments, the above-described determination subunit is further configured to: setting a probability of a domain included in the domain identification result to a fixed probability value in response to determining that at least one domain is included in the domain identification result; determining final probability values of a plurality of fields according to the probability values of the fields represented by the elements of the multidimensional vector and the fixed probability values of the fields included in the field identification result; and determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
In some embodiments, the domain classification model is trained by: obtaining a sample set, wherein the sample comprises a sample multidimensional vector and a sample sentence; and taking the sample sentences in the sample set as input, taking the sample multidimensional vectors corresponding to the input sample sentences as expected output, and training to obtain the domain classification model.
In some embodiments, the samples in the sample set are obtained by: using the at least one semantic analysis service to perform semantic analysis on the sample sentence; and generating a sample multidimensional vector for the sample sentence according to the analysis result of the semantic analysis service in the at least one semantic analysis service.
In a third aspect, embodiments of the present disclosure provide a server comprising: one or more processors; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method as described in any of the implementations of the first aspect.
The embodiment of the disclosure provides a method and a device for generating information, firstly, receiving dialogue content input by a user. Then, the domain to which the dialogue content belongs is determined based on the dialogue content. Then, according to the domain to which the dialogue content belongs, a semantic parsing service is determined from at least one semantic parsing service as a target semantic parsing service. And finally, carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result. Therefore, resource waste caused by calling all semantic analysis services is avoided.
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Other features, objects and advantages of the present disclosure will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;
FIG. 2 is a flow chart of one embodiment of a method for generating information according to the present disclosure;
FIG. 3 is a schematic illustration of one application scenario of a method for generating information according to the present disclosure;
FIG. 4 is a flow chart of yet another embodiment of a method for generating information according to the present disclosure;
FIG. 5 is a schematic structural diagram of one embodiment of an apparatus for generating information according to the present disclosure;
FIG. 6 is a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which a method for generating information or an apparatus for generating information of embodiments of the present disclosure may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a man-machine conversation application, a web browser application, a shopping class application, a search class application, a map class application, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting man-machine interaction, including but not limited to smartphones, tablet computers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for information presented on the terminal devices 101, 102, 103. The background server may analyze and the like the received data such as the conversation content and feed back the processing result (e.g., a sentence generated for the received conversation content) to the terminal devices 101, 102, 103.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster formed by a plurality of servers, or as a single server. When server 105 is software, it may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should be noted that, the method for generating information provided by the embodiments of the present disclosure is generally performed by the server 105, and accordingly, the apparatus for generating information is generally provided in the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for generating information according to the present disclosure is shown. The method for generating information comprises the following steps:
in step 201, dialog content entered by a user is received.
In this embodiment, an execution subject of the method for generating information (e.g., the server 105 shown in fig. 1) may receive dialogue content from a terminal with which a user performs man-machine interaction through a wired connection or a wireless connection. Here, the dialogue content may include text information, voice information, picture information, and the like. As an example, in the case where the dialog content includes picture information, the execution subject may perform semantic recognition on the picture, generating text information related to the picture. In the case where the dialog content includes voice information, the execution subject may perform voice recognition on the voice information, generating text information related to the voice. Then, the subsequent processing is performed on the basis of the obtained text information. It is obvious that the technology of semantic recognition of pictures and the technology of speech recognition of speech are well known technologies widely studied and applied at present, and therefore are not described here in detail.
In this embodiment, the execution subject may determine, based on the dialog content received in step 201, the domain to which the dialog content belongs. In practice, a plurality of fields, for example, a weather field, a music field, a movie field, a map field, and the like, may be divided in advance according to actual business needs. The execution body may determine the domain to which the dialog content entered by the user belongs in various ways. As one example, the execution subject may classify the dialog content input by the user using a classification model trained in advance based on a machine learning algorithm, thereby obtaining a domain to which the dialog content belongs. As another example, the execution body may store one keyword set in advance for each domain. In this way, the execution body may perform word segmentation processing on the dialogue content input by the user, match a plurality of words obtained by the word segmentation processing with keywords in a keyword set corresponding to each domain, and determine a domain to which the dialogue content belongs from the plurality of domains according to a matching result. For example, a domain corresponding to a keyword set including the most word among a plurality of words may be determined as a domain to which the dialogue content belongs.
And 203, determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs.
In this embodiment, the execution body may determine the semantic parsing service from the at least one semantic parsing service as the target semantic parsing service according to the domain to which the dialog content determined in step 202 belongs. Here, each of the at least one semantic parsing service may be used to perform semantic parsing on content of a specific domain. As an example, the execution body may take the domain determined in step 202 as a target domain, and then select a semantic parsing service for processing contents of the target domain from at least one semantic parsing service as a target semantic parsing service.
In practice, for each of a plurality of domains that are pre-partitioned, the execution subject may invoke a corresponding semantic parsing service. In practical use, for each domain, there is a corresponding semantic analysis service, which can perform semantic analysis on the content in the domain, but cannot perform semantic analysis on the content in other domains. For example, for a music domain, there is a corresponding semantic parsing service that can perform semantic parsing on the content of the input music domain, and if the input content is not the music domain, the semantic parsing service cannot perform parsing. Here, the semantic analysis service is called when man-machine interaction is performed at present, and is a known technology widely studied and applied at present, so that a detailed description is omitted here.
And 204, carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result.
In this embodiment, the execution body may invoke the target semantic parsing service to perform semantic parsing on the dialog content, thereby generating a semantic parsing result. After that, the execution body may perform various processes based on the semantic analysis result, for example, the execution body may generate a feedback sentence for the dialog content according to the preset business logic and the semantic analysis result, and send the feedback sentence to the terminal device used by the user, so as to implement man-machine interaction.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for generating information according to the present embodiment. In the application scenario of fig. 3, the user first inputs the dialogue content "what is the weather of tomorrow a? The terminal device 301 transmits the session content to the server 302. Then, the server 302 determines, based on the dialogue content, that the domain to which the dialogue content belongs is "weather domain". Then, the server 302 determines a semantic parsing service from at least one semantic parsing service as a target semantic parsing service according to a domain "weather domain" to which the dialog content belongs, where the target semantic parsing service is used for performing semantic parsing on the content in the weather domain. Finally, the server 302 invokes the target semantic parsing service to perform semantic parsing on the dialog content, thereby generating a semantic parsing result.
The method provided by the embodiment of the disclosure firstly determines the field to which the dialogue content input by the user belongs, and then invokes the semantic analysis service corresponding to the determined field to perform semantic analysis on the dialogue content, thereby avoiding resource waste caused by invoking all the semantic analysis services.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating information is shown. The flow 400 of the method for generating information comprises the steps of:
in step 401, dialog content entered by a user is received.
In this embodiment, step 401 is similar to step 201 of the embodiment shown in fig. 2, and will not be described here again.
In the present embodiment, the execution subject may have a domain classification model built therein in advance. The domain classification model may be used to characterize the correspondence of text content to multidimensional vectors. Thus, the execution body may input the dialog content received in step 401 into the domain classification model, thereby obtaining a multidimensional vector. Here, a plurality of dimensions in the obtained multidimensional vector are in one-to-one correspondence with a plurality of fields set in advance, and values of elements of the multidimensional vector are probability values of fields to which the dialogue content belongs.
Here, the domain classification model may be a multi-classifier trained based on a machine learning algorithm, and takes a domain as a classification class. In a real human-machine conversation scenario, there may be a field ambiguity in the form of the conversation content input by the user. For example, the user enters dialog content "rained", which relates to both the weather view "rained" and to certain songs named "rained". The dialogue content belongs to the weather field and the music field. In order to solve the problem that the dialog content input by the user has field ambiguity, the activating function of the output layer of the field classification model can use a sigmod function, namely, probability of each category in the multi-classification task is between 0 and 1, and probability values of each category are independent. Here, a plurality of dimensions in the multidimensional vector output by the domain classification model are in one-to-one correspondence with a plurality of preset domains, and the value of each element of the multidimensional vector is a probability value that the text content belongs to the corresponding domain. Taking four fields of weather field, music field, film field and map field as examples, the multidimensional vector may include four dimensions, which are respectively in one-to-one correspondence with the four fields of weather field, music field, film field and map field. Specifically, the value of the first element of the multidimensional vector represents a probability value that the text content belongs to the weather field, the value of the second element represents a probability value that the text content belongs to the music field, the value of the third element represents a probability value that the text content belongs to the film field, and the value of the fourth element represents a probability value that the text content belongs to the map field. It should be noted that the types, the numbers, and the like of the fields in this example are merely illustrative, and not limiting, and in actual use, the types, the numbers, and the like of the fields may be set according to actual needs.
Taking a domain classification model as a multi-classifier built based on a convolutional neural network as an example, during training, input text contents can be divided according to words, each division is converted into vectors through a word embedding representation layer, so that vectorization of the input text contents is realized, vector representation of the input text contents is obtained, the convolutional neural network can automatically perform feature learning and extraction on the vectorized text contents, and feature dimension reduction is performed through a MaxPooling layer (a maximum pooling layer), semantic vector dimension is mapped to category numbers through a full-connection layer, and the probability that the text contents belong to each category is obtained through a sigmod function by each dimension vector. Here, cross entropy may be used as a loss function, one for each category, and multiple classification is achieved by learning multiple classification tasks by sharing the same network.
In some alternative implementations of the present embodiment, the domain classification model may be trained by:
first, a sample set is acquired.
In this implementation, the execution body for training the domain classification model may obtain a sample set, and each sample in the sample set may include a sample multidimensional vector and a sample sentence. The values of the elements of the sample multidimensional vector are probability values of the corresponding fields of the sample sentence. Here, the sample multidimensional vector in the sample may be artificially generated by a worker according to the field to which the sample sentence belongs.
Then, a sample sentence in the sample set is taken as input, a sample multidimensional vector corresponding to the input sample sentence is taken as expected output, and the field classification model is trained and obtained.
In this implementation manner, the execution body may train to obtain the domain classification model by taking a sample sentence in the sample set as an input and taking a sample multidimensional vector corresponding to the input sample sentence as an expected output.
In some alternative implementations, the samples in the sample set described above may be obtained by:
first, a sample sentence is semantically parsed using at least one semantic parsing service.
In this implementation manner, for a sample sentence of a sample in the sample set, the execution body may call the at least one semantic analysis service to perform semantic analysis on the sample sentence, so as to obtain an analysis result of each semantic analysis service. In practice, since each semantic analysis service is used for performing semantic analysis on content in a specific domain, if a domain to which an input sample sentence belongs is not a specific domain corresponding to the semantic analysis service, the semantic analysis service cannot analyze the input sample sentence, and at this time, the analysis result is null.
Then, a sample multidimensional vector for the sample sentence is generated according to an analysis result of the semantic analysis service in the at least one semantic analysis service.
In this implementation, the execution body may generate a sample multidimensional vector for the sample sentence according to an analysis result of each of the at least one semantic analysis service. As an example, for each of the at least one semantic parsing service, if the parsing result returned by the semantic parsing service is not null, setting a value of an element in the sample multidimensional vector, the element having the same corresponding field as the specific field corresponding to the semantic parsing service, to 1; if the analysis result returned by the semantic analysis service is null, setting the value of the element which corresponds to the field and is the same as the specific field corresponding to the semantic analysis service in the sample multidimensional vector to be 0. In this way, the execution body can generate a sample multidimensional vector with values of 0 and 1 for the elements. According to the implementation mode, the execution main body can automatically generate the sample multidimensional vector aiming at the sample sentence, and manual intervention is not needed, so that the generation efficiency of the sample is improved.
In this embodiment, for some fields, a semantic template set may be preset inside the execution body, where the semantic templates in the semantic template set may include slots and text segments. Here, the slot may be key information that the human-machine conversation system needs to collect to the user. The slot filling is a process of collecting user information, is a process of complementing the fuzzy or missing intention of the user, and the slot position value is specific key information expressed by the user. Taking the music domain as an example, one semantic template for the music domain may be "play [ song name ]", where play is a text segment and [ song name ] is a slot. In this way, the executing body can match the dialogue content with the semantic templates in the semantic template set preset for the field, so as to judge whether the dialogue content is matched with the semantic templates. As an example, the execution body may first match whether text segments in the semantic templates are included in the dialog content, and if so, may further perform slot recognition in a variety of ways, such as slot recognition based on named entity recognition, slot recognition based on dictionary values, and so forth. If the dialog content is identified to include slot values that satisfy the slots in the semantic template, it may be determined that the dialog content matches the semantic template.
Thereafter, the execution body may determine a domain identification result for the dialog content according to the matching result. As an example, the execution subject may take a domain corresponding to a semantic template that matches the dialog content as a domain identification result for the dialog content.
In some application scenarios, the execution body may also receive semantic templates set for the domain and store the semantic templates to a set of semantic templates. Therefore, staff can intervene in the domain division of the user input content by sending the semantic templates to the execution main body, and the controllability of the result is ensured.
In some optional implementations of this embodiment, the determining the domain identification result for the dialog content based on the matching result in step 403 may specifically be performed as follows:
first, in response to determining that the dialog content matches a semantic template in the set of semantic templates, a weight of text in the dialog content that matches the semantic template over text corresponding to the dialog content is calculated.
In this implementation, if it is determined that the dialog content matches a semantic template in the set of semantic templates, the execution subject may calculate a proportion of text in the dialog content that matches the semantic template to text corresponding to the dialog content. Taking dialogue content as 'please play XXX', wherein 'XXX' is song name, the dialogue content is matched with a semantic template 'play [ song name ]', text matched with the semantic template in the dialogue content is 'play XXX' 5 words, text corresponding to the dialogue content is 'please play XXX' 6 words, and calculated specific gravity is 5/6.
Then, in response to determining that the specific gravity exceeds a preset specific gravity threshold, the domain corresponding to the matched semantic template is used as a domain recognition result.
In this embodiment, the specific gravity threshold value may be preset in the execution body, and the specific gravity threshold value may be set according to actual needs. In this way, the execution subject may compare the calculated specific gravity with the specific gravity threshold value, determine whether the calculated specific gravity exceeds (i.e., is greater than) the specific gravity threshold value, and if so, use the domain corresponding to the matched semantic template as the domain recognition result. According to the implementation mode, the execution main body can calculate the proportion of the text matched with the semantic template in the dialogue content to the text corresponding to the dialogue content, and the domain corresponding to the matched semantic template is used as the domain recognition result only when the proportion is greater than the proportion threshold value, so that the determined domain recognition result can be more accurate.
In this embodiment, the execution body may determine the domain to which the dialog content belongs, based on the multidimensional vector obtained in step 402 and the domain identification result obtained in step 403. As an example, the execution body may first use, as a first domain, a domain corresponding to a dimension in which a value of an element in the multidimensional vector obtained in step 402 is greater than a preset threshold, and use, as a second domain, a domain included in the domain identification result obtained in step 403, where the preset threshold may be set according to actual needs. Thereafter, the execution body may determine the first domain and the second domain as domains to which the dialog content belongs.
In some alternative implementations of the present embodiment, the step 404 may specifically be performed as follows:
first, in response to determining that at least one domain is included in the domain identification result, a probability of the domain included in the domain identification result is set to a fixed probability value.
In this implementation manner, the execution subject may first determine whether the domain is included in the domain identification result, and if so, set the probability of the domain included in the domain identification result to a fixed probability value, where the fixed probability value may be set according to actual needs, for example, the fixed probability value may be set to 1.
Then, final probability values of the plurality of domains are determined based on the probability values of the domains represented by the elements of the multidimensional vector and the fixed probability values of the domains included in the domain identification result.
In this implementation, the execution body may determine final probability values of the plurality of domains according to the probability values of the domains represented by the elements in the multidimensional vector and the fixed probability values of the domains included in the domain identification result. As an example, the execution subject may first determine a probability value corresponding to the same domain as the domain included in the domain identification result in the multidimensional vector, compare the probability value with a fixed probability value, and select a larger value of the two probability values as a final probability value. As an example, taking a multi-dimensional vector as a three-dimensional vector (0.5,0.9,0.1), fields represented by elements of the three-dimensional vector are a field a, a field B, and a field C, respectively, and a fixed probability value of the field a included in the field recognition result is "1", it may be determined that a final probability value of the field a may be a fixed probability value of "1", and final probability values of the fields B "and C may be 0.9 and 0.1, respectively.
And finally, determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
In this implementation manner, for each of the multiple domains, the execution subject may store a corresponding probability threshold value in advance, and the probability threshold value may be set according to actual needs. In this way, the execution subject can determine the domain to which the dialogue content belongs according to the final probability values of the plurality of domains and the probability threshold value preset for each domain. As an example, the execution subject may select, as the domain to which the dialog content belongs, a domain whose final probability value is greater than the corresponding probability threshold value, among the plurality of domains. Taking the example that the final probability values of the multiple domains including the domain a, the domain B and the domain C are 1, 0.9 and 0.1, and the probability thresholds of the domain a, the domain B and the domain C are 0.9, 0.8 and 0.7, respectively, the execution subject can select the domain a and the domain B as the domains to which the dialogue content belongs. By the implementation mode, the determined field of the dialogue content can be more accurate.
And step 405, determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs.
In this embodiment, step 405 is similar to step 203 of the embodiment shown in fig. 2, and will not be described here again.
And 406, carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result.
In this embodiment, step 406 is similar to step 204 of the embodiment shown in fig. 2, and will not be described here again.
As can be seen from fig. 4, compared to the corresponding embodiment of fig. 2, the flow 400 of the method for generating information in this embodiment highlights the step of determining the domain to which the dialog content belongs based on the domain classification model and the semantic template. Therefore, the scheme described in the embodiment can enable the determined field of the dialogue content to be more accurate, and further enable the generated semantic analysis result to be more accurate.
With further reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present disclosure provides an embodiment of an apparatus for generating information, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied in various electronic devices.
As shown in fig. 5, the apparatus 500 for generating information of the present embodiment includes: a receiving unit 501, a first determining unit 502, a second determining unit 503, and a generating unit 504. Wherein the receiving unit 501 is configured to receive dialogue content input by a user; the first determining unit 502 is configured to determine, based on the dialog content, a domain to which the dialog content belongs; the second determining unit 503 is configured to determine, according to a domain to which the above-described dialogue content belongs, a semantic parsing service from at least one semantic parsing service as a target semantic parsing service, where the semantic parsing service is used for performing semantic parsing on the content in a specific domain; the generating unit 504 is configured to perform semantic parsing on the dialog content using the target semantic parsing service, and generate a semantic parsing result.
In this embodiment, the specific processes and the technical effects of the receiving unit 501, the first determining unit 502, the second determining unit 503, and the generating unit 504 of the apparatus 500 for generating information may refer to the relevant descriptions of the steps 201, 202, 203, and 204 in the corresponding embodiment of fig. 2, and are not repeated herein.
In some optional implementations of this embodiment, the first determining unit 502 includes: an input unit (not shown in the figure) configured to input the dialog content into a pre-established domain classification model to obtain a multidimensional vector, wherein a plurality of dimensions in the multidimensional vector are in one-to-one correspondence with a plurality of preset domains, and values of elements of the multidimensional vector are probability values of the dialog content belonging to the corresponding domains; a matching unit (not shown in the figure) configured to match the dialog content with a semantic template in a semantic template set preset for a domain, and to determine a domain identification result for the dialog content based on the matching result; a determining subunit (not shown in the figure) configured to determine, based on the multidimensional vector and the domain identification result, a domain to which the dialog content belongs.
In some optional implementations of this embodiment, the matching unit is further configured to: in response to determining that the dialog content matches a semantic template in the set of semantic templates, calculating a weight of text in the dialog content that matches the semantic template to text corresponding to the dialog content; in response to determining that the specific gravity exceeds a preset specific gravity threshold, the domain corresponding to the matched semantic template is used as a domain identification result.
In some optional implementations of this embodiment, the determining subunit is further configured to: setting a probability of a domain included in the domain identification result to a fixed probability value in response to determining that at least one domain is included in the domain identification result; determining final probability values of a plurality of fields according to the probability values of the fields represented by the elements of the multidimensional vector and the fixed probability values of the fields included in the field identification result; and determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
In some optional implementations of this embodiment, the domain classification model is trained by: obtaining a sample set, wherein the sample comprises a sample multidimensional vector and a sample sentence; and taking the sample sentences in the sample set as input, taking the sample multidimensional vectors corresponding to the input sample sentences as expected output, and training to obtain the domain classification model.
In some optional implementations of this embodiment, the samples in the sample set are obtained by: using the at least one semantic analysis service to perform semantic analysis on the sample sentence; and generating a sample multidimensional vector for the sample sentence according to the analysis result of the semantic analysis service in the at least one semantic analysis service.
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., server in fig. 1) 600 suitable for use in implementing embodiments of the present disclosure is shown. The server illustrated in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure in any way.
As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing means 601.
It should be noted that, the computer readable medium according to the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In an embodiment of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Whereas in embodiments of the present disclosure, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving dialogue content input by a user; determining a domain to which the dialogue content belongs based on the dialogue content; determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs, wherein the semantic analysis service is used for carrying out semantic analysis on the content in the specific domain; and carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result.
Computer program code for carrying out operations of embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments described in the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a receiving unit, a first determining unit, a second determining unit, and a generating unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the receiving unit may also be described as "a unit that receives dialogue content input by a user".
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (12)
1. A method for generating information, comprising:
receiving dialogue content input by a user;
based on the dialogue content, determining a domain to which the dialogue content belongs, including: inputting the dialogue content into a pre-established domain classification model to obtain a multidimensional vector, wherein a plurality of dimensions in the multidimensional vector are in one-to-one correspondence with a plurality of preset domains, and the values of elements of the multidimensional vector are probability values of the corresponding domains of the dialogue content; matching the dialogue content with semantic templates in a semantic template set preset for the field, and determining a field recognition result for the dialogue content based on the matching result, wherein the semantic templates in the semantic template set comprise slots and text fragments; determining the domain to which the dialogue content belongs based on the multidimensional vector and the domain identification result;
Determining a semantic analysis service from at least one semantic analysis service as a target semantic analysis service according to the domain to which the dialogue content belongs, wherein the semantic analysis service is used for carrying out semantic analysis on the content in the specific domain;
and carrying out semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result.
2. The method of claim 1, wherein the determining a domain identification result for the dialog content based on the matching result comprises:
calculating the proportion of texts matched with the semantic templates in the dialogue content to the texts corresponding to the dialogue content in response to the fact that the dialogue content is matched with the semantic templates in the semantic template set;
and responding to the fact that the specific gravity exceeds a preset specific gravity threshold, and taking the domain corresponding to the matched semantic template as a domain identification result.
3. The method of claim 1, wherein the determining, based on the multidimensional vector and the domain identification result, a domain to which the dialog content belongs comprises:
in response to determining that at least one domain is included in the domain identification result, setting a probability of the domain included in the domain identification result to a fixed probability value;
Determining final probability values of a plurality of fields according to the probability values of the fields represented by the elements of the multidimensional vector and the fixed probability values of the fields included in the field identification result;
and determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
4. The method of claim 1, wherein the domain classification model is trained by:
obtaining a sample set, wherein the sample comprises a sample multidimensional vector and a sample sentence;
and taking the sample sentences in the sample set as input, taking the sample multidimensional vectors corresponding to the input sample sentences as expected output, and training to obtain the domain classification model.
5. The method of claim 4, wherein the samples in the sample set are obtained by:
using the at least one semantic parsing service to perform semantic parsing on the sample sentence;
and generating a sample multidimensional vector for the sample sentence according to the analysis result of the semantic analysis service in the at least one semantic analysis service.
6. An apparatus for generating information, comprising:
A receiving unit configured to receive dialogue content input by a user;
a first determination unit configured to determine, based on the dialogue content, a domain to which the dialogue content belongs;
a second determining unit configured to determine, from at least one semantic analysis service, a semantic analysis service as a target semantic analysis service according to a domain to which the dialogue content belongs, wherein the semantic analysis service is used for performing semantic analysis on the content in a specific domain;
the generation unit is configured to perform semantic analysis on the dialogue content by using the target semantic analysis service to generate a semantic analysis result;
wherein the first determining unit includes: the input unit is configured to input the dialogue content into a pre-established domain classification model to obtain multi-dimensional vectors, wherein a plurality of dimensions in the multi-dimensional vectors are in one-to-one correspondence with a plurality of preset domains, and values of elements of the multi-dimensional vectors are probability values of the corresponding domains of the dialogue content; a matching unit configured to match the dialogue content with semantic templates in a semantic template set preset for a domain, and determine a domain recognition result for the dialogue content based on the matching result, wherein the semantic templates in the semantic template set comprise slots and text fragments; and a determination subunit configured to determine, based on the multidimensional vector and the domain identification result, a domain to which the dialog content belongs.
7. The apparatus of claim 6, wherein the matching unit is further configured to:
calculating the proportion of texts matched with the semantic templates in the dialogue content to the texts corresponding to the dialogue content in response to the fact that the dialogue content is matched with the semantic templates in the semantic template set;
and responding to the fact that the specific gravity exceeds a preset specific gravity threshold, and taking the domain corresponding to the matched semantic template as a domain identification result.
8. The apparatus of claim 6, wherein the determination subunit is further configured to:
in response to determining that at least one domain is included in the domain identification result, setting a probability of the domain included in the domain identification result to a fixed probability value;
determining final probability values of a plurality of fields according to the probability values of the fields represented by the elements of the multidimensional vector and the fixed probability values of the fields included in the field identification result;
and determining the domain to which the dialogue content belongs according to the final probability values of the domains and probability thresholds preset for the domains.
9. The apparatus of claim 6, wherein the domain classification model is trained by:
Obtaining a sample set, wherein the sample comprises a sample multidimensional vector and a sample sentence;
and taking the sample sentences in the sample set as input, taking the sample multidimensional vectors corresponding to the input sample sentences as expected output, and training to obtain the domain classification model.
10. The apparatus of claim 9, wherein the samples in the sample set are obtained by:
using the at least one semantic parsing service to perform semantic parsing on the sample sentence;
and generating a sample multidimensional vector for the sample sentence according to the analysis result of the semantic analysis service in the at least one semantic analysis service.
11. A server, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
12. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-5.
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